https://huggingface.co/HuggingFaceM4/tiny-random-LlamaForCausalLM ```python from sparseml.transformers import SparseAutoModelForCausalLM, SparseAutoTokenizer, oneshot from sparseml.modifiers import SparseGPTModifier model_id = "HuggingFaceM4/tiny-random-LlamaForCausalLM" compressed_model_id = "mgoin/tiny-random-LlamaForCausalLM-pruned95-compressed" # Apply SparseGPT to the model oneshot( model=model_id, dataset="open_platypus", recipe=SparseGPTModifier(sparsity=0.95), output_dir="temp-output", ) model = SparseAutoModelForCausalLM.from_pretrained("temp-output", torch_dtype="auto") tokenizer = SparseAutoTokenizer.from_pretrained(model_id) model.save_pretrained(compressed_model_id.split("/")[-1], save_compressed=True) tokenizer.save_pretrained(compressed_model_id.split("/")[-1]) # Upload the checkpoint to Hugging Face from huggingface_hub import HfApi HfApi().upload_folder( folder_path=compressed_model_id.split("/")[-1], repo_id=compressed_model_id, ) ```